Determining a price for a good or service is a task which almost all businesses must engage in at one point or another during their existence. In fact, in many cases, pricing is one of the most important areas for businesses to focus on in a competitive market, as pricing decisions directly affect revenue and profitability and hence business value. Pricing various goods and services, however, is not a trivial task. In pricing various goods or services it may be desirable to account for a myriad number of factors including business constraints or strategies. These factors may be interdependent or discrete, known or unknown, etc. Furthermore, in many cases these businesses may have a large number of products or product combinations; and, for these products, multiple price lists that are used to communicate price offers to different types of customers. In addition, the specific set of products that are on offer can change intensively as new products are developed and old products are obsolete. Market conditions, such as customer demand for the products, competitor offers and prices, may also change rapidly and significantly.
As can be seen, then, to set the right prices for hundreds or thousands of products for different customer types under different conditions may be a challenging task, as the factors for each of these scenarios may vary to a greater or lesser degree and may be interdependent. What is desired in many processing scenarios is a price list which may be used to communicate pricing offers to potential customers.
Thus, to set prices for these price lists businesses often employ some kind of mathematical logic or model, usually some form of parametric demand model. These types of price optimization solutions are typically based on obtaining demand or price sensitivity estimates for pricing segments by applying regression models to historical data under some assumption about the functional form of the relationship between quantity purchased and price (a demand curve). In particular, a particular shape may be assumed for a particular demand curve (for example logistic) and the curve calibrated by tailoring the parameters of the assumed model to account for the historical data using statistical techniques (for example least squares or maximum likelihood estimation). A price point at which revenue or profit is maximized can then be determined using this calibrated curve.
These types of solutions are based on, and motivated by, microeconomic theory describing the behavior of consumers in a market and other assumptions. Such an approach may be useful if the sales process which gives rise to the historical transactions conforms to the assumptions of the statistical techniques, however, these methodologies are not without their problems.
More specifically, in certain contexts such as a business to business pricing environment, transactions are complex. These transactions frequently involve multiple units of individual products, negotiations may characterize the price setting, product cycles are short and market conditions are volatile. Thus, the estimated parameters and assumptions of pricing models are often found to be incomplete, inaccurate or unreliable.
Furthermore, price offers are rarely determined independently of the outcome, purchase quantities on a specific transaction are rarely decided in response to the price offer, and reliable information on rejected offers (“losses”) is rarely available. In these cases then it is difficult to generate price recommendations using such price sensitivity estimates from regression models.
Additionally, many of the mathematical models used to calculate pricing recommendations may require assumptions which may be difficult to determine or estimate, such as market boundaries, sizes of market, etc. Many of these assumptions do not adhere in real world scenarios. Consequently, these flawed assumptions may reduce the accuracies of the price model and hence the determination of a revenue or profit maximizing price point. While certain more complex mathematical models may be utilized to calibrate a parametric demand model or determine price points with more or less pricing information, these solutions do not altogether ameliorate the deficiencies of many of these prior art price determination systems or methods.
As a result of these limitations and deficiencies, in many cases not only are these types prior art solutions inadequate, but in addition they may require a substantial amount of customization for a particular scenario and human intervention during the implementation of the process.
The use of human judgments is, however, also problematic. As determining price lists may be quite complex, human involvement increases the possibilities of errors and omissions and increases the time required to determine price values and update lists which, in turn, causes infrequent updates. Human judgment may also be unreliable in effectively determining which price values support particular strategies or objectives and may entail a reliance on specialists to perform the analysis.
Accordingly, improved systems and methods for determining pricing recommendations, which may be used in a price list or for other purposes, are desired.